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Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening
In structure-based virtual screening, compound ranking through a consensus of scores from a variety of docking programs or scoring functions, rather than ranking by scores from a single program, provides better predictive performance and reduces target performance variability. Here we compare tradit...
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| 出版年: | J Chem Inf Model |
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| 主要な著者: | , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2017
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5872818/ https://ncbi.nlm.nih.gov/pubmed/28654262 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1021/acs.jcim.7b00153 |
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